code: def test_multi_input_ablation_with_layer_mask(self) -> None:
      net = BasicModel_MultiLayer_MultiInput()
      inp1 = torch.tensor([[23.0, 100.0, 0.0], [20.0, 50.0, 30.0]])
      inp2 = torch.tensor([[20.0, 50.0, 30.0], [0.0, 100.0, 0.0]])
      inp3 = torch.tensor([[0.0, 100.0, 10.0], [2.0, 10.0, 3.0]])
      baseline = torch.tensor([[1.0, 2.0, 3.0]])
      layer_mask = torch.tensor([[0, 1, 0], [0, 1, 2]])
      self._ablation_test_assert(
          net,
          net.model.linear1,
          (inp1, inp2, inp3),
          [[316.0, 992.0, 316.0], [84.0, 632.0, 120.0]],
          additional_input=(1,),
          baselines=baseline,
          perturbations_per_eval=(1, 2, 3),
          layer_mask=layer_mask,
          attribute_to_layer_input=True,
      )
      self._ablation_test_assert(
          net,
          net.model.linear0,
          (inp1, inp2, inp3),
          [[316.0, 992.0, 316.0], [84.0, 632.0, 120.0]],
          additional_input=(1,),
          baselines=baseline,
          layer_mask=layer_mask,
          perturbations_per_eval=(1, 2, 3),
      )
  
file_path: captum/tests/attr/layer/test_layer_ablation.py
imports: import unittest
  from typing import Any, List, Tuple, Union
  import torch
  from captum._utils.typing import BaselineType
  from captum.attr._core.layer.layer_feature_ablation import LayerFeatureAblation
  from tests.helpers.basic import assertTensorTuplesAlmostEqual, BaseTest
  from tests.helpers.basic_models import BasicModel_ConvNet_One_Conv, BasicModel_MultiLayer, BasicModel_MultiLayer_MultiInput
  from torch import Tensor
  from torch.nn import Module
label_desc: Use image node_id 3 for calling the Test obj's underlying member method code with example usage: obj.test_multi_input_ablation_with_layer_mask() without return types